Learning Believable Player Movement Patterns from Human Data in a Soccer Game

被引：0

作者：

Khaustov, Victor ^{[1
]}

Mozgovoy, Maxim ^{[1
]}

机构：

[1] Univ Aizu, Act Knowledge Engn Lab, Aizu Wakamatsu, Fukushima, Japan

来源：

2020 22ND INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT): DIGITAL SECURITY GLOBAL AGENDA FOR SAFE SOCIETY! | 2020年

关键词：

soccer; player tracking; believability; human-like AI; case-based reasoning;

D O I：

10.23919/icact48636.2020.9061246

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Player movement patterns are one of the behavioral traits immediately visible to an observer. Thus, a soccer AI system striving for believable (human-like) behavior must ensure the believability of player movements. We show how tracking data of real human players in soccer can be used to create a case-based reasoning AI system, able to simulate realistic player movements in a computer soccer game. Our results are confirmed with a direct comparison of actions made by AI-controlled players and professional athletes.

引用

页码：91 / 93

页数：3

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